deeplotx 0.4.12b1

Easy-2-use long text NLP toolkit.

Deep Long Text Learning Kit

Author: 吴子豪

开箱即用的长文本语义建模框架

安装

• 使用 pip

  pip install -U deeplotx
  

• 使用 uv (推荐)

  uv add -U deeplotx
  

• 从 github 安装最新特性

  pip install -U git+https://github.com/vortezwohl/DeepLoTX.git
  

核心功能

• 长文本嵌入

  • 基于通用 BERT 的长文本嵌入 (最大支持长度, 无限长, 通过 max_length 定义)

    from deeplotx import LongTextEncoder
    
    # 最大文本长度为 2048 个 tokens, 块大小为 512 个 tokens, 块间重叠部分为 64 个 tokens.
    encoder = LongTextEncoder(
        max_length=2048,
        chunk_size=512,
        overlapping=64
    )
    # 对 "我是吴子豪, 这是一个测试文本." 计算嵌入, 并展平.
    encoder.encode('我是吴子豪, 这是一个测试文本.', flatten=True, use_cache=True)
    

    输出:

    tensor([ 0.5163,  0.2497,  0.5896,  ..., -0.9815, -0.3095,  0.4232])
    

  • 基于 Longformer 的长文本嵌入 (最大支持长度 4096 个 tokens)

    from deeplotx import LongformerEncoder
    
    encoder = LongformerEncoder()
    encoder.encode('我是吴子豪, 这是一个测试文本.')
    

• 相似性计算

  • 基于向量的相似性

    import deeplotx.similarity as sim
    
    vector_0, vector_1 = [1, 2, 3, 4], [4, 3, 2, 1]
    # 欧几里得距离
    distance_0 = sim.euclidean_similarity(vector_0, vector_1)
    print(distance_0)
    # 余弦距离
    distance_1 = sim.cosine_similarity(vector_0, vector_1)
    print(distance_1)
    # 切比雪夫距离
    distance_2 = sim.chebyshev_similarity(vector_0, vector_1)
    print(distance_2)
    

    输出:

    4.47213595499958
    0.33333333333333337
    3
    

  • 基于集合的相似性

    import deeplotx.similarity as sim
    
    set_0, set_1 = {1, 2, 3, 4}, {4, 5, 6, 7}
    # 杰卡德距离
    distance_0 = sim.jaccard_similarity(set_0, set_1)
    print(distance_0)
    # Ochiai 距离
    distance_1 = sim.ochiai_similarity(set_0, set_1)
    print(distance_1)
    # Dice 系数
    distance_2 = sim.dice_coefficient(set_0, set_1)
    print(distance_2)
    # Overlap 系数
    distance_3 = sim.overlap_coefficient(set_0, set_1)
    print(distance_3)
    

    输出:

    0.1428571428572653
    0.2500000000001875
    0.25000000000009376
    0.2500000000001875
    

  • 基于概率分布的相似性

    import deeplotx.similarity as sim
    
    dist_0, dist_1 = [0.3, 0.2, 0.1, 0.4], [0.2, 0.1, 0.3, 0.4]
    # 交叉熵
    distance_0 = sim.cross_entropy(dist_0, dist_1)
    print(distance_0)
    # KL 散度
    distance_1 = sim.kl_divergence(dist_0, dist_1)
    print(distance_1)
    # JS 散度
    distance_2 = sim.js_divergence(dist_0, dist_1)
    print(distance_2)
    # Hellinger 距离
    distance_3 = sim.hellinger_distance(dist_0, dist_1)
    print(distance_3)
    

    输出:

    0.3575654913778237
    0.15040773967762736
    0.03969123741566945
    0.20105866986400994
    

• 预定义深度神经网络

  from deeplotx import (
      LinearRegression,  # 线性回归
      LogisticRegression,  # 逻辑回归 / 二分类 / 多标签分类
      SoftmaxRegression,  # Softmax 回归 / 多分类
      RecursiveSequential,  # 序列模型 / 循环神经网络
      AutoRegression  # 自回归模型
  )
  

  基础网络结构:

  from typing_extensions import override
  
  import torch
  from torch import nn
  
  from deeplotx.nn.base_neural_network import BaseNeuralNetwork
  
  
  class LinearRegression(BaseNeuralNetwork):
      def __init__(self, input_dim: int, output_dim: int, model_name: str | None = None):
          super().__init__(model_name=model_name)
          self.fc1 = nn.Linear(input_dim, 1024)
          self.fc1_to_fc4_res = nn.Linear(1024, 64)
          self.fc2 = nn.Linear(1024, 768)
          self.fc3 = nn.Linear(768, 128)
          self.fc4 = nn.Linear(128, 64)
          self.fc5 = nn.Linear(64, output_dim)
          self.parametric_relu_1 = nn.PReLU(num_parameters=1, init=5e-3)
          self.parametric_relu_2 = nn.PReLU(num_parameters=1, init=5e-3)
          self.parametric_relu_3 = nn.PReLU(num_parameters=1, init=5e-3)
          self.parametric_relu_4 = nn.PReLU(num_parameters=1, init=5e-3)
  
      @override
      def forward(self, x) -> torch.Tensor:
          fc1_out = self.parametric_relu_1(self.fc1(x))
          x = nn.LayerNorm(normalized_shape=1024, eps=1e-9)(fc1_out)
          x = torch.dropout(x, p=0.2, train=self.training)
          x = self.parametric_relu_2(self.fc2(x))
          x = nn.LayerNorm(normalized_shape=768, eps=1e-9)(x)
          x = torch.dropout(x, p=0.2, train=self.training)
          x = self.parametric_relu_3(self.fc3(x))
          x = torch.dropout(x, p=0.2, train=self.training)
          x = self.parametric_relu_4(self.fc4(x)) + self.fc1_to_fc4_res(fc1_out)
          x = self.fc5(x)
          return x